Long-term Time Series Forecasting based on Decomposition and Neural Ordinary Differential Equations
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Abstract (arXiv)
Long-term time series forecasting (LTSF) is a challenging task that has been investigated in various domains such as finance investment, health care, traffic, and weather forecasting. In recent years, Linear-based LTSF models showed better performance, pointing out the problem of Transformer-based approaches causing temporal information loss. However, Linear-based approach has also limitations that the model is too simple to comprehensively exploit the characteristics of the dataset. To solve these limitations, we propose LTSF-DNODE, which applies a model based on linear ordinary differential equations (ODEs) and a time series decomposition method according to data statistical characteristics. We show that LTSF-DNODE outperforms the baselines on various real-world datasets. In addition, for each dataset, we explore the impacts of regularization in the neural ordinary differential equation (NODE) framework.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 31 of its 50 references that are in the map. First posted on arXiv 8 Nov 2023.
Builds on
31 of 50 references.
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19 more
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